Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet often lack a robust methodology to dissect these phenomena comprehensively. Our work aims to bridge this gap by delving into the Food domain, a universally relevant yet culturally diverse aspect of human life. We introduce FmLAMA, a multilingual dataset centered on food-related cultural facts and variations in food practices. We analyze LLMs across various architectures and configurations, evaluating their performance in both monolingual and multilingual settings. By leveraging templates in six different languages, we investigate how LLMs interact with language-specific and cultural knowledge. Our findings reveal that (1) LLMs demonstrate a pronounced bias towards food knowledge prevalent in the United States; (2) Incorporating relevant cultural context significantly improves LLMs' ability to access cultural knowledge; (3) The efficacy of LLMs in capturing cultural nuances is highly dependent on the interplay between the probing language, the specific model architecture, and the cultural context in question. This research underscores the complexity of integrating cultural understanding into LLMs and emphasizes the importance of culturally diverse datasets to mitigate biases and enhance model performance across different cultural domains.

翻译：近期研究揭示了大语言模型（LLMs）中存在的文化偏见，但往往缺乏系统性的方法论来全面解析这些现象。本研究旨在通过探索人类生活中具有普遍相关性又兼具文化多样性的饮食领域来弥补这一空白。我们构建了FmLAMA——一个以饮食文化事实与饮食习俗差异为核心的多语言数据集。我们分析了多种架构与配置的LLMs，评估其在单语及多语环境下的表现。通过使用六种不同语言的提示模板，我们深入探究了LLMs如何与语言特异性知识及文化知识产生交互。研究发现：（1）LLMs对盛行于美国的饮食知识表现出显著偏好；（2）引入相关文化语境能显著提升LLMs获取文化知识的能力；（3）LLMs捕捉文化细微差异的效果，高度依赖于提示语言、特定模型架构与文化语境三者间的相互作用。本研究揭示了将文化理解整合到LLMs中的复杂性，并强调了采用文化多样性数据集对于缓解偏见、提升模型跨文化领域表现的重要性。